The present invention generally relates to a system and method for obtaining and using synthesized fault data for pump diagnosis and failure prediction.
Motors, pumps and bearings require frequent maintenance attention in typical commercial systems and industrial plants. With conventional maintenance strategies such as exception-based and periodic-checking, faults developed in critical equipment (e.g. pumps) have to be detected by human experts through physical examination and other off-line tests (e.g., metal wear analysis) during a routine maintenance in order for corrective action to be taken. Faults that go undetected during a regular maintenance check-up may lead to catastrophic failure and un-scheduled shut-down of the plant. The probability of an un-scheduled shut-down increases as the time period between successive maintenance inspections increases. The frequency of performing maintenance, however, is limited by availability of man-power and financial resources and hence is not easily increased. Some maintenance inspections, such as impeller inspection may require stopping the process or even disassembling machinery. The lost production time may cost ten times more than the labor cost involved. There is also a possibility that the reassembled machine may fail due to an assembly error or high start up stresses for example. Finally, periodically replacing components (via routine preventive maintenance) such as bearings, seals, or impellers is costly since the service life of good components may unnecessarily be cut short.
Cavitation, blockage and impeller damage are common problems/faults encountered with pumps. Cavitation can cause accelerated wear, and mechanical damage to pump components, couplings, gear trains, and drive motors. Cavitation is the formation of vapor bubbles in the inlet flow regime or the suction zone of the pump. This condition occurs when local pressure drops to below the vapor pressure of the liquid being pumped. These vapor bubbles collapse or implode when they enter a high pressure zone (e.g. at the discharge section or a higher pressure area near the impeller) of the pump causing erosion of impeller casings as well as other pump components. If a pump runs for an extended period under cavitation conditions, permanent damage may occur to the pump structure and accelerated wear and deterioration of pump internal surfaces and seals may occur. Detection of such conditions before they become severe or prolonged can help to avoid cavitation-induced damage to the pump and facilitate extended plant up time. Such detection also can avoid accelerated pump wear and unexpected failures and further enable a well planned and cost-effective maintenance routine. Depending on the type of pump, other problems can occur such as inlet or outlet blockage, leakage of air into the system due to faulty pump seals, or the impeller or impeller parts impacting the pump casing.
Prior efforts in pump diagnostics have included vibration analysis and acoustic analysis techniques. For example, a modern chemical plant may require a service engineer to physically go to hundreds or even thousands of critical pumps periodically (e.g., monthly) to record vibration data from the pump. The data is then subsequently analyzed using vibration analysis algorithms to detect pump problems such as broken impeller vanes or out of balance conditions. Other research efforts have looked at performing pump diagnostics using process instrumentation such as flow meters and pressure transducers. Some efforts have looked at the relationship between inlet and outlet pressures and flow rate with pump speed to determine if a pump problem exists. Others have performed trending on these parameters over time.
Still other techniques have focused on signal analysis of unconditioned process sensors, such as flow sensors and pressure sensors. Flow sensors such as orifice-plate differential pressure, vortex, turbine, time-domain pressure techniques are invasive sensors and must be installed in-line within the process framework or pump system. Other flow sensors such as corriolis flow meters, are extremely costly and must be installed in-line with process piping.
In view of the above, there is a strong need in the art for a system and/or method for condition monitoring which mitigates some of the above-noted problems associated with conventional pump monitoring systems and/or methods.
The present invention provides for a system and method for condition monitoring of synthesized fault data and determining an operating condition of a pump. It has been found that fault data relating to the operating condition of a pump is encoded in variations in current of a motor driving the pump. These features present in the stator frequency spectrum of the motor stator current are caused by load effects of the pump on the motor rather than changes in the motor itself. The present invention provides a system and method for extracting (e.g., synthesizing) the fault data directly from the instantaneous motor current data. This data relates not only to pump machinery conditions, but also pump process conditions. Thus, by employing current signature analysis of the instantaneous current of the motor driving the pump, problems with the pump and/or process line can be detected without using invasive and expensive pressure and flow meters. Instead, a lower cost current sensor may be used and this sensor may be located in a motor control center or other suitable location remote from the motor and pump.
More particularly, in a preferred embodiment, the present invention includes utilizing an artificial neural network (ANN) to analyze the current signature data of the motor that relates to pump faults. Although, multi-iterative, supervised leaning algorithms could be used, which could be trained and used only when a fully-labeled data set corresponding to all possible operating conditions, the application of unsupervised ANN techniques that can learn on-line (even in a single iteration) are preferred. The present invention will be described with respect to an AC induction motor and both centrifugal and positive displacement pumps. However, it is to be appreciated that the present invention has applicability to substantially any type of pump and motor combination where current signature analysis can be performed to determine the operating state (e.g., health) of the pump. It should also be appreciated that the current signature analysis can be performed both on the pump to determine the operating state of the pump, and on the motor driving the pump to determine the operating state of the motor, simultaneously.
The present invention also provides for preprocessing of the fault signature data before it is being used to train an ANN or design a decision module based on ANN paradigms. The preprocessing eliminates outliers and performs scaling and bifurcation of the data into training and testing sets. Furthermore, it is desired to further post process the output generated by unsupervised ANN based decision modules for condition monitoring applications. This is because unsupervised ANN based decision modules when presented with a new operating condition can only signal the formation of a new output entry indicating that a possible new condition has occurred, but is not necessarily able to provide particular fault information. Post processing is carried out by utilizing the domain knowledge of a human expert to develop an expert system, or by correctly classifying this new operating state and encoding this information in a fuzzy expert system for future reference.
Furthermore, the present invention provides an intelligent, stand alone decision module which can identify the operating condition of the pump/plant without significant human supervision. The stand alone decision module employs adaptive preprocessing and intelligent post processing in conjunction with a one shot unsupervised ANN algorithm. Preferably, the ANN algorithm is either an adaptive resonance theory (ART-2) or associative list memory (ALM) algorithm. Typical operating characteristics related to the motor, pump and fault signatures is used to define the default limits to be used in the adaptive preprocessing and for designing a Fuzzy Rule Based Expert System (FRBES) for post processing in the condition monitoring of the pump.
It should be appreciated that the present invention can be utilized to analyze both the motor and the pump from a motor-pump system and identify fault modes. It should be further appreciated that the present invention can be employed to condition monitor multiple pumps and motors simultaneously.
In accordance with one particular aspect of the present invention, a system for monitoring the condition of a pump driven by a motor is provided. A sensor is operatively coupled to a power lead of the motor, the sensor is adapted to obtain at least one current signal relating to the operation of the pump. An artificial neural network is operatively coupled to the sensor, the artificial neural network being adapted to detect at least one fault relating to the operation of the pump from the at least one current signal.
Another aspect of the present invention relates to a method for monitoring the condition of a pump driven by a motor. A first sample of current data signal relating to the operation of the pump is collected. The first sample of current data signal is input to a neural network. A second sample of current data signal relating to the operation of the pump is collected. The second sample of current data signal is input to the neural network, wherein any differences between the first signal and the second signal will be generated as a change in condition signal by the neural network, any change of condition.
Still yet another aspect of the present invention relates to a stand alone decision module adapted to receive a current signal from a machine and facilitate diagnosing the state of the machine by determining if the current signal contains fault data relating to the state of the machine. A neural network is operatively coupled to a sensor, the neural network is adapted to synthesize a change in condition signal from the sampled current data. A preprocessing portion is operatively coupled to the neural network, the preprocessing portion is adapted to condition the current signal prior to inputting the current signal into the neural network. A post processing portion is operatively coupled to the neural network, the post processing portion is adapted to determine whether the change in condition signal is due to a fault condition related to the state of the machine.
Another aspect of the present invention relates to a system for simultaneously monitoring the condition of a pump driven by a motor and the condition of the motor driving the pump. A sensor is operatively coupled to a power source of the motor, the sensor is adapted to obtain at least one current signal relating to the operation of the pump and the operation of the motor. An artificial neural network is operatively coupled to the sensor, the artificial neural network being adapted to detect at least one fault relating to the operation of the pump and at least one fault relating to the operation of the motor from the at least one current signal.
Another aspect of the present invention relates to a system for diagnosing a plurality of pumps, each driven by a motor. A plurality of sensors obtain current data signals from each motor. A channel interface is operatively coupled to the plurality of sensors, the channel interface designating a separate channel for each of the plurality of sensors. A host computer is operatively coupled to the channel interface, the host computer including a neural network operatively coupled to each channel of the channel interface, the neural network adapted to detect at least one fault relating to the operation of the plurality of pumps from the current data signals, wherein a processor of the host computer cycles through each of the channels, the processor performing classical signature analysis on each of the plurality of pumps using the current data signal for each respective pump.
Another aspect of the present invention relates to a system for monitoring the condition of a pump driven by a motor, including means for detecting at least one current signal from the motor, said at least one current signal containing fault attributes related to the condition of the pump; means for extracting the fault attributes from the at least one current signal; means for determining if the fault attributes signify a fault condition of the pump; and means for communicating any fault conditions to a system operator.
Another aspect of the present invention relates to a system for monitoring the condition of a pump driven by a motor. A sensor is operatively coupled to a power lead of the motor, the sensor is adapted to obtain at least one current signal relating to the operation of the pump. A processor is operatively coupled to the sensor, the processor is adapted to convert the at least one current signal to a frequency spectrum having a plurality of fault attributes related to the condition of the pump and preprocess the fault attributes. An unsupervised artificial neural network is operatively coupled to the processor, the artificial neural network being adapted to recognize and detect changes in the plurality of preprocessed fault attributes and provide a change of condition pattern relating to changes in the plurality of preprocessed fault attributes. A post processor includes decision making rules for determining fault conditions of the pump based on the change of condition pattern, the post processor communicating any fault conditions to a system operator.
To the accomplishment of the foregoing and related ends, the invention, then, comprises the features hereinafter fully described and particularly pointed out in the claims. The following description and the annexed drawings set forth in detail certain illustrative embodiments of the invention. These embodiments are indicative, however, of but a few of the various ways in which the principles of the invention may be employed. Other objects, advantages and novel features of the invention will become apparent from the following detailed description of the invention when considered in conjunction with the drawings.